the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Jan 2026
Local weather scenarios for soil and crop models: a simple generator based on historic data sampling
Abstract. Weather scenarios are for example required to model future agricultural production and the development of soil properties under climate change. These scenarios should realistically depict regional weather conditions at a daily resolution for the expected climate development. In this technical note, we present the LocalWeatherSampler (LWS) for generating mid-term weather scenarios (20–30 years) for specific regions or locations based on historically recorded weather data. It is demonstrated for an example site in Germany. The core idea is to define wet or dry years and to increase their abundance in future years via a random sampling from history. A temperature trend based on common climate projections can be added afterwards. For the definition of dry/wet years, two different methods are implemented. The historical weather data can be either divided manually into a pool of wet (or dry) years or based on the Standardized Precipitation Index (SPI). By varying the threshold value for wet (dry) years and their probability of appearance within the scenario, the framework allows for the generation of scenarios tailored to specific requirements, such as sequences characterized by extremely dry years or by moderately dry years, as well as extremely wet future sequences. This approach is designed to test or analyze future scenarios of precipitation regimes and temperature trends using models that require realistic daily weather data, such as soil, crop, or hydrological models.
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Status: open (until 23 Mar 2026)
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RC1: 'Comment on egusphere-2025-6173', Anonymous Referee #1, 24 Feb 2026
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CC1: 'Reply on RC1', Sara König, 03 Mar 2026
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Dear Reviewer, thanks for reviewing our manuscript.
You are right, the presented weather generator is based on simple R methods. It was developed, as mentioned in the manuscript, to generate weather scenarios with daily data which include realistic weather extremes such as droughts fo any specific location (i.e. arable field). We need such data as input for soil-crop modeling, which is our expertise. As we are no experts in climate modelling, we did intense literature research and discussed with various colleagues with expertise in climate modelling (e.g. from DWD and Herion), and had to conclude that there is no tool available serving our purpose. This short technical note is thought to provide a simple solution for this gap that might help others. If you are aware of a similar tool, we are more than happy to include it to our work.
Regarding the statistics: could you please elaborate more on which kind of statistical test you have in mind? Note that we do not aim to predict climate, but to generate site-specific realistic weather dynamics.Citation: https://doi.org/10.5194/egusphere-2025-6173-CC1
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CC1: 'Reply on RC1', Sara König, 03 Mar 2026
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RC2: 'Comment on egusphere-2025-6173', Anonymous Referee #2, 13 Mar 2026
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This technical note provides a description of a simple and user-friendly applicable code to distribute weather scenarios for a pre-determined duration. At first, pools of drier and wetter years will be set on which randomly precipitation and temperature scenarios will be set thereafter. It is very good that the scripts are published. In general, it is a very required tool, as the authors mention the need for such data sets for soil–crop models. However, the manuscript stresses some climatically terms too much while no definitions are provided.
I recommend avoiding the terms ‘dry’ and ‘wet’ and rather using them in a relative sense, for example as ‘drier’ and “wetter” or as ‘relatively dry’ and ‘comparatively wet’. E.g., an area with very high amounts of precipitation (e.g., >1500 mm) and a threshold of 0.8 wouldn’t make the pool of “drier” years “dry”. The same applies to the word arid. Often, aridity is defined as higher (evapo)transpiration than precipitation. If this is not your case, don’t use the term “arid” throughout the manuscript unless you provide a proper definition of how you define it.
Why is only temperature corrected according to RCP scenarios? If temperature is adjusted to trends, shouldn’t precipitation scenarios also have a trend?
If this technical note creates data sets for precipitation and temperature, why is this not also done for solar radiation when you want to use such scenarios for soil–crop modelling? For most of these models this weather information is totally mandatory. Please explain very briefly how that was achieved. If not, explain briefly why it is not important for this study case. Otherwise, don’t mention the models that require “solar radiation”.
Figure 2 and Table 2: Align your naming of the scenarios.
L73 – Robust results? How is robustness determined?
L113 – Your tool doesn’t control “aridity”!
L125 and L140 – Explain how the manual setting can be done or what the difference is compared to “manually” selecting an RCP scenario. It is always a “manual” selection. What is the actual difference between the selection modes.
Citation: https://doi.org/10.5194/egusphere-2025-6173-RC2
Model code and software
R script: Local weather scenarios for soil and crop models: a simple generator based on historic data sampling Stefan Anton Albert Gasser et al. https://doi.org/10.5281/zenodo.17511186
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Reviewing of the manuscript titled ‘Local weather scenarios for soil and crop models: a simple generator based on historic data sampling’ submitted to the discussion of Geoscientific Model Development (Manuscript Number: egusphere-2025-6173). The authors develop a simple generator based on the historic precipitation data for the local weather scenarios (e.g., wet or dry). The time period is long and fine (e.g., 1993-2022 or historic weather and 2023-2052 for projections). The manuscript is overly simple and written in a study that is not strict and not including sufficient literature review and discussion. In my opinion, this is a simple formulation using the R statistics for the data analysis and case study, which lacks strong novelty to fill the research gaps from previous studies. There are also a couple of specific comments. For example, (1) in Figure 1, the reviewer is confused that why in step 4 for Future Scenarios the selected years still 2001-2021? (2) the paragraph should be line well instead of randomly being located, such as in Section 2.1; (3) Is there any statistics to evaluate the validity and accuracy of the proposed model or method? The authors need substantial works to include more details in each section.